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Functional evaluation of domain–domain interactions and human protein interaction networks
- Bioinformatics
, 2007
"... Abstract: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology to evaluate the molecular func ..."
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Abstract: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple Gene Ontology terms. Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. 1
Integrated prediction of the helical membrane protein interatome in
"... At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein– protein interactions are bi ..."
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At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein– protein interactions are biased against membrane proteins. Here, we define protein–protein interaction broadly as co-complexation, and develop a weighted-voting procedure to predict interactions among yeast helical membrane proteins by optimally combining evidence based on diverse genome-wide information such as sequence, function, localization, abundance, regulation, and phenotype. We use logistic regression to simultaneously optimize the weights of all evidence sources for best discrimination based on a set of known helical membrane protein interactions. The resulting integrated classifier not only significantly outperforms classifiers based on any single genomic feature, but also does better than a benchmark Naïve Bayes classifier (using a simplifying assumption of conditional independence among features). Finally, we apply the optimized classifier genome-wide, and construct a comprehensive map of predicted helical membrane protein interactome in yeast. This can serve as a guide for prioritizing further experimental validation efforts. q 2006 Published by Elsevier Ltd.
Computational Biology
"... USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of ..."
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USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of
Giorgio: Random Walking on Functional Interaction Networks to Rank Genes
- Involved in Cancer. Artificial Intelligence Applications and Innovations
, 2012
"... Abstract. A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Modules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, ..."
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Abstract. A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Modules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, we show that random walks and label propagation algorithms are able to correctly rank genes with respect to CMs. In particular, the random walk with restart algorithm (RWR), by exploiting both the global topology of the functional interaction network, and local functional connections between genes relatively close to CM genes, achieves significantly better results than the other compared methods, suggesting that RWR could be applied to discover novel genes involved in the biological processes underlying tumoral diseases.
DASMIweb: online integration, analysis and
, 2009
"... assessment of distributed protein interaction data ..."
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BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm012 Systems biology
"... Functional evaluation of domain–domain interactions and human protein interaction networks ..."
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Functional evaluation of domain–domain interactions and human protein interaction networks
Chapter 23 Computational Representation of Biological Systems
"... Integration of large and diverse biological data sets is a daunting problem facing systems biology researchers. Exploring the complex issues of data validation, integration, and representation, we present a systematic approach for the management and analysis of large biological data sets based on da ..."
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Integration of large and diverse biological data sets is a daunting problem facing systems biology researchers. Exploring the complex issues of data validation, integration, and representation, we present a systematic approach for the management and analysis of large biological data sets based on data warehouses. Our system has been implemented in the Bioverse, a framework combining diverse protein information from a variety of knowledge areas such as molecular interactions, pathway localization, protein structure, and protein function.
Chapter 22 The Bioverse API and Web Application
"... The Bioverse is a framework for creating, warehousing and presenting biological information based on hierarchical levels of organisation. The framework is guided by a deeper philosophy of desiring to represent all relationships between all components of biological systems towards the goal of a wholi ..."
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The Bioverse is a framework for creating, warehousing and presenting biological information based on hierarchical levels of organisation. The framework is guided by a deeper philosophy of desiring to represent all relationships between all components of biological systems towards the goal of a wholistic picture of organismal biology. Data from various sources are combined into a single repository and a uniform interface is exposed to access it. The power of the approach of the Bioverse is that, due to its inclusive nature, patterns emerge from the acquired data and new predictions are made. The implementation of this repository (beginning with acquisition of source data, processing in a pipeline, and concluding with storage in a relational database) and interfaces to the data contained in it, from a programmatic application interface to a user friendly web application, are discussed.
Chapter 10 Inferring Molecular Interactions Pathways from eQTL Data
"... Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute the changes in expression levels to loci on the genome, not specific genes. Each locus can contain m ..."
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Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute the changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring the graphs of molecular interactions. We show several simple methods can find complete pathways that explain the mechanism of differential expression in eQTL data. Key words: eQTL, pathway inference, gene regulation, signaling pathways. 1.
